ArticleJournal of biomedical optics2023
Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues
Article in Journal of biomedical optics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery.NPJ digital medicine · 2025Article
- Rapid detection of soybean nutrient deficiencies with YOLOv8s for precision agriculture advancement.Scientific reports · 2025Article
- Telepresence for surgical assistance and training using eXtended reality during and after pandemic periods.Journal of telemedicine and telecare · 2025Article
- Current landscape and future trends in salivary gland oncology research-a bibliometric evaluation.Gland surgery · 2024Article
- Switchable LED-based laparoscopic multispectral system for rapid high-resolution perfusion imaging.Journal of biomedical optics · 2023Article
- End-to-end deep learning classification of vocal pathology using stacked vowels.Laryngoscope investigative otolaryngology · 2023Article
- Bibliometric analysis of the current status and trends on medical hyperspectral imaging.Frontiers in medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: Accurate identification of tissues is critical for performing safe surgery. Combining multispectral imaging (MSI) with deep learning is a promising approach to increasing tissue discrimination and classification. Evaluating the contributions of spectral channels to tissue discrimination is important for improving MSI systems. Aim: Develop a metric to quantify the contributions of individual spectral channels to tissue classification in MSI. Approach: MSI was integrated into a digital operating microscope with three sensors and seven illuminants. Two convolutional neural network (CNN) models were trained to classify 11 head and neck tissue types using white light (RGB) or MSI images. The signal to noise ratio (SNR) of spectral channels was compared with the impact of channels on tissue classification performance as determined using CNN visualization methods. Results: Overall tissue classification accuracy was higher with use of MSI images compared with RGB images, both for classification of all 11 tissue types and binary classification of nerve and parotid ( Conclusions: The spectral channel SNR is a useful metric for both understanding CNN tissue classification and quantifying the contributions of different spectral channels in an MSI system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.